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graph partitioning

Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.

Papers

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Showing 101–125 of 208 papers

TitleStatusHype
Stochastic Blockmodeling for Online Advertising—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
Supporting Very Large Models using Automatic Dataflow Graph Partitioning—0
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
Towards Efficient Large-Scale Graph Neural Network Computing—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
VLSI Hypergraph Partitioning with Deep Learning—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
WawPart: Workload-Aware Partitioning of Knowledge Graphs—0
Weighted Laplacian and Its Theoretical Applications—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
Distributed Training of Large Graph Neural Networks with Variable Communication Rates—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering—0
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm—0
Initialization for Network Embedding: A Graph Partition Approach—0
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
_2-norm Flow Diffusion in Near-Linear Time—0
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware—0
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